关键词: 分类, K-近邻算法, 相似度

Abstract: An improved KNN algorithm based on contribution of attribute value and average similarity is proposed，which is different from the traditional K-nearest neighbor algorithm.This paper not only considers the average similarity between the test samples and the similar samples but also considers the number of the similar samples in different categories.And finally considers the contribution of the same attribute value to the class label.The experiments is performed on mushroom data sets.The experimental results show that the approach has higher accuracy than the traditional K-nearest neighbor algorithm.

Key words: classification, K-Nearest Neighbor algorithm（KNN）, similarity